Addressing Grand Challenges in Organization Theory-System Change through Theory, Engagement & Action
Bibliographic record
Abstract
The Covid-19 pandemic warrants increased scholarly attention to grand challenges, such as inequality, poverty, climate change, ecological imbalances, socioeconomic and political crises along with their extended impacts (Davis, 2020; Ferraro, Etzio, & Gehman, 2015; George et al., 2016; Munir, 2020; Pio & Waddock, 2020). Whereas mainstream organizational theorizing brings those challenges in as part of our empirical contexts, or in our managerial implications, this symposium will discuss how we can address head-on the importance of grand challenges, make them a core aspect of our research, and accordingly develop theoretical and empirical approaches that may be seen as a ‘grand challenges turn’ in our field. Some recent examples include work on the patriarchy of microfinance (Zhao & Wry, 2016) and providing toilets in Indian villages (Mair, Wolf, & Seelos, 2016). Nevertheless, even scholars working on grand challenges tend to focus on a particular problem and may lose sight of the broader systemic challenges of these issues (Munir, 2019).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.082 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".